Image credit: CDC's Public Health Image Library (PHIL)

Corresponding lectures

Lecture 13 - Single cell RNA-seq – principles and processing Lecture 14 - Analysis of scRNA-seq data using R


In the lecture this week, you learned to do ~90% of the common tasks associated with analyzing single cell RNA-seq (scRNA-seq) data, including QC analysis, producing UMAP dimensional reductions, labeling cell clusters, and identifying cluster-defining genes. You then applied this to spleen samples from naive and Toxoplasma gondii infected mice. In this lab, we’ll review these steps and extend what we’ve learned to brain samples from the same mice (all courtesy of Chris Hunter’s lab).

What you’ll need to do

To get started, download this seurat object. This is scRNA-seq data from the brain of a single naive mouse, integrated with data from a single mouse infected with Toxoplasma gondii. I’ve already run this through ScaleData, RunPCA, RunUMAP, FindNeighbors, and FindClusters in Seurat.

Of course, you’ll also need to download the course DIY_scRNAseq.R script

Finally, you’ll want to download some reference data from the Azimuth project. Specifically, download the Mouse Motor Cortex idx.annoy and ref.Rds files from here. You’ll use this data to apply labels to the cell clusters in this lab.

Part 1

Read the integrated brain Seurat object into your R environment and create a UMAP. I recommend using a new clean environment


DimPlot(brain_integrated, reduction = "umap", = "treatment", # this facets the plot = "seurat_clusters", # labels the cells with values from your variable
        label = TRUE)

Part 2

Use the Azimuth Mouse Motor Cortex reference data you downloaded above to apply labels to the clusters in your UMAP. If you have any issues with Azimuth, just use CellDex and SingleR to apply labels instead.

Part 3 (Bonus)

Choose a cluster from your labeled UMAP and use Seurat findMarkers to compare gene expression between the cells in this cluster in naive vs infected mice.